Network Functions Virtualization: The Long Road to Commercial Deployments

Rehman, A. U.; Aguiar, Rui L.; Barraca, João Paulo

doi:10.1109/access.2019.2915195

Cited by 38 publications

(30 citation statements)

References 115 publications

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“…The concept of implementing an NF as a VM has been commonly referred to as Virtualized Network Function (VNF), and Network Function Virtualization (NFV) as a broader term for the technology of implementing, deploying, and managing the VNFs. In general, the NFV concept has been widely discussed in the survey literature [45]- [47]. The traditional challenges of NFV deployment are associated with the virtualization process of NFs, such as overhead, isolation, resource allocation, and function management [48].…”

Section: Contributions and Organization Of This Surveymentioning

confidence: 99%

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

2020

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In order to facilitate flexible network service virtualization and migration, network functions (NFs) are increasingly executed by software modules as so-called "softwarized NFs" on General-Purpose Computing (GPC) platforms and infrastructures. GPC platforms are not specifically designed to efficiently execute NFs with their typically intense Input/Output (I/O) demands. Recently, numerous hardwarebased accelerations have been developed to augment GPC platforms and infrastructures, e.g., the central processing unit (CPU) and memory, to efficiently execute NFs. This article comprehensively surveys hardware-accelerated platforms and infrastructures for executing softwarized NFs. This survey covers both commercial products, which we consider to be enabling technologies, as well as relevant research studies. We have organized the survey into the main categories of enabling technologies and research studies on hardware accelerations for the CPU, the memory, and the interconnects (e.g., between CPU and memory), as well as custom and dedicated hardware accelerators (that are embedded on the platforms); furthermore, we survey hardware-accelerated infrastructures that connect GPC platforms to networks (e.g., smart network interface cards). We find that the CPU hardware accelerations have mainly focused on extended instruction sets and CPU clock adjustments, as well as cache coherency. Hardware accelerated interconnects have been developed for on-chip and chip-to-chip connections. Our comprehensive up-to-date survey identifies the main trade-offs and limitations of the existing hardware-accelerated platforms and infrastructures for NFs and outlines directions for future research.

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Section: Contributions and Organization Of This Surveymentioning

confidence: 99%

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

2020

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“…NFV leverages virtualization techniques to enable the flexible design, deployment, and management of network functions, independent of the underlying physical network equipment [98]- [100]. These network functions may include classical functions, such as firewalls, deep packet inspection, the elements of the Evolved Packet Core (EPC), which is a framework to provide converged voice and data on LTE networks, but also innovative functions, including network coding, data aggregation, or computation as a service.…”

Section: ) Network Function Virtualization (Nfv)mentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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“…by the application of traditional techniques over the years. NFV brings flexibility, easy deployment, dynamic adjustment on demand, and easy and faster up-gradation [4]. NFV offers a new way to design, deploy, and manage networking services by decoupling the network functions, such as network address translation, firewalls, intrusion detection, domain name service, etc., from dedicated hardware devices so they can run in software [1], [3].…”

Section: Introductionmentioning

confidence: 99%

Communication and Computing Cost Optimization of Meshed Hierarchical NFV Datacenters

Kar

Lin³

2020

IEEE Access

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Telecommunication carriers of 5G-MEC are re-architecting their central offices and mobile base stations as datacenters with network function virtualization (NFV) technology. These datacenters (DCs) are known as edge datacenters that help network operators speed deployment and reduce costs. Previously, the use of NFV was limited to within a datacenter (DC) known as intra-DC. Recently, many studies have been conducted into the use of NFV across DCs, i.e., inter-DC. However, these NFV inter-DC architectures have limited communication between DCs with either horizontal or vertical connectivity. In this paper, we propose a generic architecture of such edge NFV datacenters with both horizontal and vertical connectivity, and demonstrate the consequences of both vertical and horizontal connectivity between DCs in terms of communication and computing costs. We formulate a cost optimization problem with latency and capacity as constraints by estimating the traffic dispatch rate between DCs. We propose a vertical-horizontal communication (VHC) heuristic solution to the NP-hard problem. Compared to horizontal connectivity, our results show that vertical connectivity helps to reduce computing costs by 10-30%. However, both vertical and horizontal communications together can help to reduce such costs by 20-30% compared to only vertical communication. INDEX TERMS Inter-DC connectivity, VNF placement, service chaining, communication, computing. BINAYAK KAR (Member, IEEE) received the Ph.D. degree in computer science and information engineering from National Central University

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Network Functions Virtualization: The Long Road to Commercial Deployments

Cited by 38 publications

References 115 publications

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Communication and Computing Cost Optimization of Meshed Hierarchical NFV Datacenters

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